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Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that the proposed method can achieve an accuracy exceeding 96% for satellite visibility prediction and obtain better generalization performance than existing multipath prediction methods. Moreover, the attention weights among satellites were visualized to demonstrate the environment representation learned by the GTNN from the sky satellite graph.

Abstract This study investigates the efficacy of NKG2D chimeric antigen receptor (CAR) engineered T cells in targeting and eliminating stress-induced senescent cells in vitro. Cellular senescence contributes to age-related tissue decline and is characterized by permanent cell cycle arrest and the senescence-associated secretory phenotype (SASP). Immunotherapy, particularly CAR-T cell therapy, emerges as a promising approach to selectively eliminate senescent cells. Our focus is on the NKG2D receptor, which binds to ligands (NKG2DLs) upregulated in senescent cells, offering a target for CAR-T cells. Using mouse embryonic fibroblasts (MEFs) and astrocytes (AST) as senescence models, we demonstrate the elevated expression of NKG2DLs in response to genotoxic and oxidative stress. NKG2D-CAR T cells displayed potent cytotoxicity against these senescent cells, with minimal effects on non-senescent cells, suggesting their potential as targeted senolytics. In conclusion, our research presents the first evidence of NKG2D-CAR T cells’ ability to target senescent brain cells, offering a novel approach to manage senescence-associated diseases. The findings pave the way for future investigations into the therapeutic applicability of NKG2D-targeting CAR-T cells in naturally aged organisms and models of aging-associated brain diseases in vivo.

Lifespan can be affected by both physiological ageing and specific sets of pathologies associated with old age. Here the authors report a resource of large-scale cross-sectional phenotyping of aging male mice at different time points to analyse a large set of phenotypes and molecular markers, including during genetic and diet interventions affecting lifespan.

A two-dimensional plasma–wave interaction model, which is based on the cold collisional plasma dielectric tensor, is applied to investigate the wave propagation and power depositions under different magnetic configurations in helicon plasmas. The varied magnetic configurations are formed by changing the radius of the magnetic coil. When the magnetic coil was positioned closer to the plasma, the magnetic field within the plasma became stronger and more curved. Consequently, the simulation results show that the wave propagation and power deposition in plasmas follow the curved magnetic field lines. In the axial direction, the periodic distribution of wave fields and power deposition are clearly observed and keep consistency in helicon plasmas due to the eigenmodes of helicon waves. Furthermore, a concave dark area where the wave cannot propagate is observed in the closest magnetic coil case and leads to limited power deposition.

In this study, an Al/Co@RDX composite was meticulously prepared through a combination of planetary high-energy ball-milling and a spray-drying technique. The thermal reactivity of these Al/Co@RDX composites was comprehensively investigated and compared using the TG/DSC technique. It is shown that the initial decomposition temperature of RDX in the DSC curve was decreased by 26.3 °C in the presence of Al/Co, which could be attributed to the nano-sized Co transition metal catalyzing the decomposition reaction of nitrogen oxides in RDX decomposition products. The decomposition peak temperature of RDX and the heat released by the thermal decomposition of RDX in the Al/Co@RDX composite were decreased by 26.3 °C and increased by 74.5 J·g−1, respectively, in comparison with those of pure RDX. The types of major gaseous products released from Al/Co@RDX were found to be identical to those of pure RDX, encompassing N2O, CH2O, CO2 and HCN. However, the concentrations of those gaseous products for Al/Co@RDX were higher than those observed for pure RDX, which may owe to the fact that the Al/Co composite can interact with the –CH2 and –NO2 within RDX molecules, which leads to the weakening of the C-N and N-N bonds. In addition, the decomposition of RDX in the Al/Co@RDX composite was observed as a one-step process with an apparent activation energy (Ea) of 115.6 kJ·cm−3. The decomposition mechanism of the RDX in the Al/Co@RDX composite was identified to follow the chain scission model (L2), whereas the two-step decomposition physical models observed for pure RDX were found to closely resemble the L2 and autocatalytic models.

Abstract Unmanned aerial vehicles as an important role for 5G and beyond networks are becoming more and more popular and have been equipped with various sensors to enable diverse emerging applications, e.g. locating sound‐emitting targets. Multi‐channel blind source separation algorithm has been applied into the unmanned aerial vehicles and micro aerial vehicles, where underdetermined mixture blind source separation is a challenging problem, i.e. the number of sources is more than the number of microphones. An optimization underdetermined blind source separation algorithm to separate the multi‐channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. In the algorithm, firstly a hierarchical clustering to estimate channel as the mixing matrix initialization is employed, while using direction of arrival permutation algorithm to deal with the permutation alignment and update the mixing matrix using multiplication update method. Then the model parameters are estimated using improved expectation‐maximization update rules for the fast convergence. Finally, the frequency‐domain sources are estimated through Wiener filtering and time‐domain sources are obtained via inverse short‐time Fourier transform. Experimental results covering synthetic and real‐recorded speech source mixtures show that the proposed algorithm achieves better separation results than the state‐of‐the‐art methods.

This paper presents a control framework of a vibrating flexible string system subject to input saturation and output constraint. By integrating the backstepping technique and Lyapunov theory, a smooth function, a Nussbaum function, a barrier function, and an auxiliary system are exploited to establish a constrained boundary control for tacking the output and input constraints as well as stabilizing the system. The derived control can assure the bounded stability in controlled systems depending on Lyapunov criteria. The simulation study and analysis verify the effectiveness of the presented scheme.

Composite modified double base (CMDB) propellants are heterogeneous propellants in which properties are significantly improved by adding solid particles into the polymer matrix. A molecular group interaction model that can predict the mechanical properties of polymers through a molecular structure is used to predict the viscoelastic behavior of the CMDB propellant. Considering that the addition of solid particles will improve the crosslinking degree between polymer molecules and reduce its secondary loss peak, the input parameters of the model are modified through dynamic mechanical analysis (DMA) experimental data. By introducing the strain rate into the expression of model glass transition temperature, the mechanical properties of propellant over a wide strain range (1.7×10−4 s-1 ~ 3000 s-1) are obtained. The reliability of the model is verified by comparison with uniaxial compression test data. By modifying the input parameters of the model, the effects of different mass ratios of nitrocellulose (NC)/nitroglycerin (NG) on the mechanical properties of the CMDB propellant were analyzed. The results show that the glass transition loss increases with increasing mass ratio of NC/NG, while Young’s modulus and yield stress decrease.

Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints.

Underdetermined blind source separation (UBSS) is a hot and challenging problem in signal processing. In the traditional UBSS algorithm, the number of source signals is often assumed to be known, which is very inconvenient in practice. In addition, it is more difficult to obtain the accurate estimation of mixing matrix in the underdetermined case. However, this information has a great influence on the source separation results, which can easily lead to poor separation performance. In this paper, a novel UBSS algorithm is presented to carry out a combined source signal number estimation and source signal separation task. First, in the proposed algorithm, we design a gap-based detection method to detect the number of source signals by eigenvalue decomposition. Then, the estimation of the mixing matrix is processed using a higher-order cumulant-based method so that the uniqueness of the estimated mixing matrix is guaranteed. Furthermore, an improved l1 -norm minimization algorithm is proposed to estimate the source signals. Meanwhile, the pre-conditioned conjugate gradient technology is employed to accelerate the convergence rate such that the computational load is reduced. Finally, a series of simulation experiments with synthetic heart sound data and image reconstruction results demonstrate that the proposed algorithm achieves better separating property than the state-of-the-art algorithms.

As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.

The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids.

Power generation can be realized in space when current is induced on a bare electrodynamic tether system. The performance of power generation is discussed based on a debris mitigation mission by numerical simulation in the paper. A Li-ion battery subsystem is used to complete the energy conversion—harvest and supply the energy. The battery can provide 10–300 W average electric power continuously during several hundred hour mission time. The energy conversion efficiency ranges from 1% to a maximum value 30%. With constant power consumption on board, the battery operation generally experiences a discharging phase, a charging phase, and a stable phase. The first two phases determine the mission risk coefficient. The heating problem in the stable phase cannot be ignored. The optimization of battery design and tether design should be considered for each debris mitigation mission. An extra control circuit or small battery voltage with large capacity for battery design is suggested to eliminate the stable phase. Wide or long tether designs are more appropriate for mission with high or low power demands on board, respectively. The power generation is affected by the system mass and the mission orbit parameters.

Energy hub integrates various energy conversion and storage technologies, which can yield complementarity among multiple energy and provide consumers with stable energy services, such as electricity, heating, and cooling. This enables energy hub to be an ideal energy system design for smart and green buildings. This paper proposes a distributed auction mechanism for multi-energy scheduling of an energy hub that serves numbers of building energy users. In the auction, users first submit their demand data to the hub manager. Then, the hub manager allocates energy to users via optimization of energy scheduling based on the users' data. The auction mechanism is designed to be incentive compatible, meaning that users are incentivized to truthfully submit their demand data. Next, to mitigate the computational burden of the hub manager, a distributed implementation of the auction is developed, in which an algorithm based on alternating direction method of multipliers (ADMM) is adopted to offload auction computation onto the users. Distributed computation offloading may bring in new chances for users to manipulate the auction outcome since the users participate part of the auction computation. It is proven that the proposed distributed auction mechanism can achieve incentive compatibility in a Nash equilibrium, which indicates that rational users will faithfully report demand data and complete the assigned computation as well. Finally, simulation results based on a household energy consumption dataset are presented to evaluate the energy scheduling performance and to verify the incentive compatibility of the auction mechanism.

Dietary restriction can extend the life of various model organisms. Here, Xie et al. show that intermittent periods of fasting achieved through every-other-day feeding protect mice against neoplastic disease but do not broadly delay organismal aging in animals.

Meter data collection and management in smart grid has the potential for underlying security risks, e.g., low-sparsity unobservable attacks. Thus, it is crucial to investigate the vulnerability of smart grid through various exposure tests associated with these unobservable attacks. Recently, much attention has been paid to low-sparsity unobservable attacks with complete knowledge of the system matrix. In this paper, the unobservable attack exposure analysis is based on a relaxed condition, i.e., an incomplete knowledge of the system matrix. Furthermore, a data-driven attack scheme is designed to demonstrate that such knowledge can be learned with a two-stage strategy. In the first stage, a sequence of intercepted meter data is utilized to learn about the incomplete system matrix with a blind identification approach. In the second stage, the estimated system matrix at hand is used for the attack vector construction with a sparsity-exploiting method. Finally, the validity of the proposed data-driven attack scheme is tested through various experiments. The proposed result reveals the potential risk of meter data leakage to the security of the smart grid.

Additive manufacturing is rapidly opening its way into many areas of the aerospace industry, where different 3D printing technologies are finding niche applications in which they do not only simplify the process and allow shorter lead times, but also the particularities of these new fabrication methods yield new material properties that enhance the component and can lead to higher performance and longer service life of an aerospace system. Although rapid manufacturing processes are being tested for in-space manufacturing and are commonly used to fabricate UAV parts and some spacecraft subsystems with 3D printed components have been tested in space, little research has been conducted on the potential application of these techniques to electric thrusters. This paper presents the study conducted on the application of selective laser melting, a powder bed fusion technology, to the fabrication of ion engine grids and the challenges faced during the process. The first proof of concept and its optimization are described. Later, the development of the selective laser melting process for molybdenum, the study of the 3D printed materials' properties, their direct application to ion extraction systems, and the tests of additively-manufactured ion optics are described. It was found that 3D printed grids can be accurately fabricated with titanium and molybdenum, that they perform similar to conventional optics in short tests, that the selective laser melting process allows certain control of the coefficient of thermal expansion of the output and that this fabrication method allows the reproduction of sputtering erosion patterns. Future research in this direction will cover sputtering tests of selectively-laser-melted samples and the additive manufacturing of carbon-carbon grids.

Predictive and prognostic biomarkers facilitate the selection of treatment strategies that can improve the survival of patients. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in cancer progression, with diagnostic and prognostic potential. However, few prognostic lncRNAs are reported for breast cancer, and little is known about their functions that contribute to cancer pathogenesis. In this paper, we used weighted correlation network analysis (WGCNA) to construct networks containing noncoding and protein-coding genes based on their expression in 1097 breast cancer patients. The differentially expressed genes were significantly overlapped with gene modules regulating cell cycle and cell adhesion. The cell cycle-related lncRNAs were consistently downregulated in breast cancer. One lncRNA, EIF3J-AS1, is significantly associated with clinicopathological characteristics, including tumor size, lymph node metastasis, estrogen receptor (ER), and progesterone receptor (PR) status. Kaplan–Meier survival analysis revealed that EIF3J-AS1, a downregulated lncRNA in breast tumor, is a potential prognostic marker for breast cancer. EIF3J-AS1 may function in an estrogen-independent manner and could be inhibited by the compound FDI-6. Therefore, integrating sparse gene coexpression network and clinicopathological features can accelerate identification and functional characterization of novel prognostic lncRNAs in breast cancer.

Alzheimer's disease (AD) is a multifactorial disorder leading to progressive memory loss and eventually death. In this study, an APPswePS1dE9 AD mouse model has been analyzed for motor cortex theta, beta and gamma frequency alterations using computerized 3D stereotaxic electrode positioning and implantable video-EEG radiotelemetry to perform long-term M1 recordings from both genders considering age, circadian rhythm and activity status of experimental animals. We previously demonstrated that APPswePS1dE9 mice exibit complex alterations in hippocampal frequency power and another recent investigation reported a global increase of alpha, beta and gamma power in APPswePS1dE9 in females of 16-17 weeks of age. In this cortical study in APPswePS1dE9 mice we did not observe any changes in theta, beta and particularly gamma power in both genders at the age of 14, 15, 18 and 19 weeks. Importantly, no activity dependence of theta, beta and gamma activity could be detected. These findings clearly point to the fact that EEG activity, particularly gamma power exhibits developmental changes and spatial distinctiveness in the APPswePS1dE9 mouse model of Alzheimer's disease.

In order to maintain robotic manipulators at a high level of performance, their controllers should be able to address nonlinearities in the closed-loop system, such as input nonlinearities. Meanwhile, computational efficiency is also required for real-time implementation. In this paper, an unknown input Bouc−Wen hysteresis control problem is investigated for robotic manipulators using adaptive control and a dynamical gain-based approach. The dynamics of hysteresis are modeled as an additional control unit in the closed-loop system and are integrated with the robotic manipulators. Two adaptive parameters are developed for improving the computational efficiency of the proposed control scheme, based on which the outputs of robotic manipulators are driven to track desired trajectories. Lyapunov theory is adopted to prove the effectiveness of the proposed method. Moreover, the tracking error is improved from ultimately bounded to asymptotic tracking compared to most of the existing results. This is of important significance to improve the control quality of robotic manipulators with unknown input Bouc−Wen hysteresis. Numerical examples including fixed-point and trajectory controls are provided to show the validity of our method.

This paper studies capacity allocation of an energy storage (ES) device which is shared by multiple homes in smart grid. Given a time-of-use (TOU) tariff, homes use the ES to shift loads from peak periods to off-peak periods, reducing electricity bills. In the proposed ES sharing model, the ES capacity has to be allocated to homes before the homes’ load data is completely known. To this end, an online ES capacity allocation algorithm is developed based on the online convex optimization framework. Under the online algorithm, the complex allocation problem can be solved round by round: at each round, the algorithm observes current system states and predicts a decision for the next round. The proposed algorithm is able to minimize homes’ costs by learning from home load data in a serial fashion. It is proven that the online algorithm can ensure zero average regret and long-term budget balance of homes. Further, a distributed implementation of the online algorithm is proposed based on alternating direction method of multipliers framework. In the distributed implementation, the one-round system problem is decomposed into multiple subproblems that can be solved by homes locally, so that an individual home does not need to send its private load data to any other. In simulation, actual home load data and a TOU tariff of the United States are used. Results show that the proposed online approach leads to the lowest home costs, compared to other benchmark approaches.

Alzheimer’s disease (AD) is a multifactorial disorder leading to progressive memory loss and eventually death. In this study an APPswePS1dE9 AD mouse model has been analyzed using implantable video-EEG radiotelemetry to perform long-term EEG recordings from the primary motor cortex M1 and the hippocampal CA1 region in both genders. Besides motor activity, EEG recordings were analyzed for electroencephalographic seizure activity and frequency characteristics using a Fast Fourier Transformation (FFT) based approach. Automatic seizure detection revealed severe electroencephalographic seizure activity in both M1 and CA1 deflection in APPswePS1dE9 mice with gender-specific characteristics. Frequency analysis of both surface and deep EEG recordings elicited complex age, gender, and activity dependent alterations in the theta and gamma range. Females displayed an antithetic decrease in theta (θ) and increase in gamma (γ) power at 18-19 weeks of age whereas related changes in males occurred earlier at 14 weeks of age. In females, theta (θ) and gamma (γ) power alterations predominated in the inactive state suggesting a reduction in atropine-sensitive type II theta in APPswePS1dE9 animals. Gender-specific central dysrhythmia and network alterations in APPswePS1dE9 point to a functional role in behavioral and cognitive deficits and might serve as early biomarkers for AD in the future.

Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.

Alzheimer’s disease (AD) has been associated with increased phosphorylation of the translation initiation factor 2α (eIF2α) at serine 51. Increased phosphorylation of eIF2α alters translational control and may thereby have adverse effects on synaptic plasticity, learning, and memory. To analyze if increased levels of p-eIF2α indeed promote AD-related neurocognitive impairments, we crossed 5xFAD transgenic mice with an eIF2αS51A knock-in line that expresses the nonphosphorylatable eIF2α variant eIF2αS51A. Behavioral assessment of the resulting mice revealed motor and cognitive deficits in 5xFAD mice that were, with the possible exception of locomotor hyperactivity, not restored by the eIF2αS51A allele. Telemetric intracranial EEG recordings revealed no measurable effects of the eIF2αS51A allele on 5xFAD-associated epileptic activity. Microarray-based transcriptome analyses showed clear transcriptional alterations in 5xFAD hippocampus that were not corrected by the eIF2αS51A allele. In contrast to prior studies, our immunoblot analyses did not reveal increased levels of p-eIF2α in the hippocampus of 5xFAD mice, suggesting that elevated p-eIF2α levels are not a universal feature of AD models. Collectively, our data indicate that 5xFAD-related pathologies do not necessarily require hyperphosphorylation of eIF2α to emerge; they also show that heterozygosity for the nonphosphorylatable eIF2αS51A allele has limited effects on 5xFAD-related disease manifestations.

A mathematical stability approach that enables the evaluation of the mulitvariate thermionic field emission parameters at Schottky barriers is presented. The method is general, requiring only the effective mass and relative dielectric constant for a given semiconductor. The approach is demonstrated in a first-time analysis of the barrier heights, tunneling probabilities and potential drops for changes in the Schottky barriers of gallium nitride nano-field effect transistors in a long-duration heavy ion radiation extreme environment. The investigation yielded fundamental insights into behavior that would be challenging to predict a priori .